As the amount of information collected in an online environment grows, individuals are increasingly protective of providing various forms of information. Accordingly, differential privacy has become an important consideration for providers that aggregate online information. In a crowdsourced, client/server environment, local differential privacy introduces randomness into user data prior to a client sharing the user data with a server. A server can learn from the aggregation of the crowdsourced data of all clients, but the server cannot learn the data provided by any particular client. As more user information is gathered, general patterns begin to emerge, which can inform and enhance the user experience. Accordingly, differential privacy provides insights from large datasets, but with a mathematical proof that information about a single individual remains private.
As described, local differential privacy introduces randomness into data, and accordingly, usage patterns are based on mathematical estimations. Depending on the size of the universe of potential user data, the variance in the accuracy of these estimations may increase in order to preserve differential privacy. Moreover, current differential privacy mechanisms are often tailored for a large universe of datasets based on the potential number of users sharing information. Accordingly, there is continued need to provide mechanisms for accurate estimation while still achieving local differential privacy of user data.